Statistical Analysis

Power analysis at the isoform level

rna-seq

RNA-Sequencing (RNA-Seq) has become a routine technology for investigating gene expression differences in comparative transcriptomic studies. Differential expression (DE) analysis of the isoforms of genes is just emerging now that expression (read counts) can be estimated with higher accuracy at ...

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How choice of analysis pipeline affects your data

rna-seq

Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation ...

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Power Analysis of Single Cell RNA Sequencing Experiments

rna-seq

High-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published ...

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scPLS – Controlling for confounding effects in single cell RNA sequencing studies using both control and target genes

rna-seq

Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling ...

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